Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks

Abstract

Hybrid methods that utilize both content and rating information are commonly used in many recommender systems. However, most of them use either handcrafted features or the bag-of-words representation as a surrogate for the content information but they are neither effective nor natural enough. To address this problem, we develop a collaborative recurrent autoencoder (CRAE) which is a denoising recurrent autoencoder (DRAE) that models the generation of content sequences in the collaborative filtering (CF) setting. The model generalizes recent advances in recurrent deep learning from i.i.d. input to non-i.i.d. (CF-based) input and provides a new denoising scheme along with a novel learnable pooling scheme for the recurrent autoencoder. To do this, we first develop a hierarchical Bayesian model for the DRAE and then generalize it to the CF setting. The synergy between denoising and CF enables CRAE to make accurate recommendations while learning to fill in the blanks in sequences. Experiments on real-world datasets from different domains (CiteULike and Netflix) show that, by jointly modeling the order-aware generation of sequences for the content information and performing CF for the ratings, CRAE is able to significantly outperform the state of the art on both the recommendation task based on ratings and the sequence generation task based on content information.

To appear at NIPS 2016

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• Revisiting Neural Retrieval on Accelerators
• A Survey on Bayesian Deep Learning
• DeepCCI: End-to-end Deep Learning for Chemical-Chemical Interaction Prediction
• Cross-domain Recommendation via Deep Domain Adaptation
• Knowledge-aware Autoencoders for Explainable Recommender Sytems
• MARS: Memory Attention-Aware Recommender System
• Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness
• Latent User Linking for Collaborative Cross Domain Recommendation
• Implicit Feedback Deep Collaborative Filtering Product Recommendation System
• Attribute-aware Collaborative Filtering: Survey and Classification
• Generating Artificial Core Users for Interpretable Condensed Data
• Coupled Variational Recurrent Collaborative Filtering